class_docstr: A generic deterministic model constructed from a callable.
  
  Example:
      >>> f = lambda x: x.sum(dim=-1, keep_dims=True)
      >>> model = GenericDeterministicModel(f)
docstr: Subset the model along the output dimension.
  
  Args:
      idcs: The output indices to subset the model to.
  
  Returns:
      The current model, subset to the specified output indices.
docstr_tok: ["Subset","the","model","along","the","output","dimension",".","Args",":","idcs",":","The","output","indices","to","subset","the","model","to",".","Returns",":","The","current","model",",","subset","to","the","specified","output","indices","."]
code: def subset_output(self, idcs: List[int]) -> GenericDeterministicModel:
      r"""Subset the model along the output dimension.
  
      Args:
          idcs: The output indices to subset the model to.
  
      Returns:
          The current model, subset to the specified output indices.
      """
  
      def f_subset(X: Tensor) -> Tensor:
          return self._f(X)[..., idcs]
  
      return self.__class__(f=f_subset, num_outputs=len(idcs))
  
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Callable, List, Optional, Union
  import torch
  from botorch.models.ensemble import EnsembleModel
  from botorch.models.model import Model
  from torch import Tensor
label_desc: Use image node_id 2 for calling the GenericDeterministicModel obj's underlying member method code with example usage: obj.subset_output(idcs) and returns: self, self